(Georgia Institute of Technology, 2023-08-28)
Alben, Noel
Web-based intelligent pedagogy systems have great potential to provide interactive music lessons to those unable to access conventional, face-to-face music instruction from human experts. A key component of any effective pedagogy system is the expert domain knowledge used to generate, present, and evaluate the teachable content that makes up the ''syllabus'' of the system (Brusilovskiy, 1994). In this work, we investigate the application of computational musicology algorithms to devise the ''syllabus'' of intelligent rhythm pedagogy software. Many computational metrics that quantify and characterize rhythmic patterns have been proposed (Toussaint). We employ Cao et al.'s (2012) family theory of rhythms as a metric of rhythmic similarity and an entropy-based coded-element metric of rhythmic complexity (Thul, 2008). Both metrics have been shown to correlate with human judgments of rhythmic similarity and complexity. A rhythmic syllabus that uses these metrics to determine the order in which rhythmic patterns are learned will be easier for musicians to progress through. We test this hypothesis in a rhythm reproduction study hosted on a custom-designed web-based experimental interface.
Our experiment consists of six individual blocks: In each block, a participant listens to five unique rhythmic patterns, which they must then reproduce by clapping into their computer's microphone. Each rhythmic pattern is two measures long on an eighth-note grid, presented at 105 BPM, and looped four times. The order and content of rhythmic patterns within each block are determined using our chosen complexity and similarity metrics. A participant completes a block when they reproduce all the rhythmic patterns of the block within the performance constraints defined by automatic performance assessment built into the experimental interface. Each of our six blocks represents key interactions: the order of the stimuli determined by our prescribed metrics, melodic information added to the rhythmic stimuli, and the presence of a visual representation of the rhythmic pattern. We also have control blocks where the patterns of each block are selected randomly without any theoretically informed metrics. Dependent variables to measure the effectiveness of the syllabus are the number of trials taken to reproduce a given rhythmic stimuli accurately. Participant reproductions are stored to afford future analyses, and the designed interface helps efficiently automate the data collection, making it more accessible for future rhythm reproduction studies.
We conducted the rhythm recreation study with 28 participants across the United States, who accessed the experiment through a web-based portal.
The data gathered from our experiment implies that computational music theory algorithms can contribute to creating syllabi that align with human perception. However, these results deviate from my initial predictions. Furthermore, It appears that while incorporating visual stimuli aided in learning rhythmic patterns, the introduction of pitched onsets negatively affected reproduction performance.